GLIOMA ANALYSIS

Emile Cohen

June 2020

Goal: In this notebook, we want to understand what makes Glioma an outlier for the patterns we saw, and what are the major subcohorts that drive the outlying signal.

In [18]:
%run -i '../../../../../utils/setup_environment.ipy'

import warnings
warnings.filterwarnings('ignore')
from scipy.stats import fisher_exact, ranksums, chi2, norm
from statsmodels.sandbox.stats.multicomp import multipletests
import matplotlib.gridspec as gridspec
import pickle

data_path = '../../../../../data/'
data_wgd = data_path + 'impact-facets-tp53/processed/wgd/'
data_no_wgd = data_path + 'impact-facets-tp53/processed/no_wgd/'

Setup environment... done!

✅ Working on **mskimpact_env** conda environment.

In [367]:
pwd
Out[367]:
'/Users/cohene/repos/mskimpact_tp53_exploration/analysis/exploratory_analysis/cancer_exploration/outliers/glioma'

Interesting functions

In [3]:
from functools import reduce

def get_hotspots(df: pd.DataFrame, Sample_Type: str, group: list = None, group_type:str = None):
    data = df[df['Sample_Type'] == Sample_Type]
    
    if group and group_type:
        data = data[data[group_type].isin(group)]

    data_1 = get_groupby(data,'tp53_spot_1', 'count'); data_2 = get_groupby(data,'tp53_spot_2', 'count'); data_3 = get_groupby(data,'tp53_spot_3', 'count') ; data_4 = get_groupby(data,'tp53_spot_4', 'count') ; data_5 = get_groupby(data,'tp53_spot_5', 'count') 
    series_data = [data_1,data_2,data_3,data_4,data_5]

    df_merged = reduce(lambda  left,right: pd.merge(left,right,left_index=True, right_index=True,
                                                how='outer'), series_data).fillna(0)

    df_merged.columns = ['count_1', 'count_2', 'count_3', 'count_4', 'count_5']
    df_merged['total'] = df_merged.sum(axis=1)
    df_merged = df_merged.sort_values(by='total', ascending=False)

    df_merged = df_merged.drop('nan')
    
    return df_merged

def get_hotspot_frac(df: pd.DataFrame, group_type:str = None, group: list = None, nb = 10):
    if group_type and group:
        df = df[df[group_type].isin(group)]
    result = [['spot', '#', 'frac']]
    for spot in get_groupby(df, 'tp53_spot_1', 'count').sort_values(by='count', ascending=False).head(nb).index.tolist():
        result.append([spot,df[df['tp53_spot_1'] == spot].frac_genome_altered.shape[0], df[df['tp53_spot_1'] == spot].frac_genome_altered.median()])

    return pd.DataFrame(result)



def boxplot_sampletype(df: pd.DataFrame, group:str, palette, order, metrics: str, figsize= (10,3), title: str = '', title_font: int=12, xlim=[0,1]):
    fig=plt.figure(figsize=figsize)
    ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)

    sns.boxplot(y=metrics, x=group,data=df,ax=ax, dodge=False,order=order, palette=palette).set_title(title, weight='bold', fontsize=title_font)
    
    groupby_ = get_groupby(df,group, 'count')
    groupby_ = groupby_.T
    for mut in mutation_list:
        if mut not in groupby_.columns:
            groupby_[mut] = 0
    groupby_ = groupby_.T
    
    labels = []
    for element in order:
        labels.append(element + '\n('+ str(groupby_.loc[element].values[0])+')')
    
    
    
    ax.set_xticklabels(labels)
    style(ax)
    ax.set_ylim(xlim)
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)

    return fig, ax
# Let's give a look at medians and statistics

def get_statistics(df: pd.DataFrame, group:str, metrics: str, group_list: list):
    group_1 = df[df[group] == group_list[0]][metrics]
    group_2 = df[df[group] == group_list[1]][metrics]

    median_1 = group_1.median()
    median_2 = group_2.median()

    statistic, p_value = ranksums(group_1.dropna().values,group_2.dropna().values)
    
    results = [['', 'size', metrics],
               [group_list[0], group_1.shape[0], median_1],
               [group_list[1], group_2.shape[0], median_2],
               ['', 'Statistics', 'p-value'],
               ['', statistic, p_value]]


    return pd.DataFrame(results)

def get_major_codrivers(master: pd.DataFrame, maf: pd.DataFrame, head:int = 10, tp53=False):
    samples = master.Tumor_Id.tolist()
    if tp53:
        maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True]
    else:
        maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True][maf['Hugo_Symbol'] != 'TP53']
    h = pd.DataFrame(maf_filtered[['Hugo_Symbol']].groupby(['Hugo_Symbol']).size())
    h.columns = ['count']
    h = h.sort_values(by='count', ascending=False).head(head)
    
    return(h)



def create_co_drivers_table(master: pd.DataFrame, group_type:str,  group_1: str, group_2: str):
    master_group_1 = master[master[group_type] == group_1]
    co_drivers_group_1 = get_major_codrivers(master=master_group_1,
                        maf=maf_cohort_nowgd,
                       head=100)
    co_drivers_group_1['proportion_1'] = co_drivers_group_1.apply(lambda x: 100* round(x['count'] / co_drivers_group_1.sum().values[0], 4), axis=1)
    
    master_group_2 = master[master[group_type] == group_2]
    co_drivers_group_2 = get_major_codrivers(master=master_group_2,
                        maf=maf_cohort_nowgd,
                       head=100)
    co_drivers_group_2['proportion_2'] = co_drivers_group_2.apply(lambda x: 100* round(x['count'] / co_drivers_group_2.sum().values[0], 4), axis=1)
    
    co_drivers_groups = pd.merge(co_drivers_group_1, co_drivers_group_2, on='Hugo_Symbol')
    co_drivers_groups['proportion_1'] = - co_drivers_groups['proportion_1']
    
    return co_drivers_groups

Master Definition and Filtering

In [4]:
cancer='Glioma'
In [5]:
master_no_wgd = non_wgd_load_and_cut(data_path + 'impact-facets-tp53/processed/no_wgd/master_no_wgd.pkl')
master_wgd = pd.read_pickle(data_path + 'impact-facets-tp53/processed/wgd/master_wgd.pkl')


master_no_wgd_cancer = master_no_wgd[master_no_wgd['Cancer_Type'] == cancer]
master_wgd_cancer = master_wgd[master_wgd['Cancer_Type'] == cancer]

maf_cohort_nowgd = pd.read_csv(data_path + 'impact-facets-tp53/processed/no_wgd/maf_cohort_nowgd.txt', sep='\t').drop('Unnamed: 0', axis=1)
maf_cohort_wgd = pd.read_csv(data_path + 'impact-facets-tp53/processed/wgd/maf_cohort_wgd.txt', sep='\t').drop('Unnamed: 0', axis=1)

What makes Glioma an Outlier?

WGD Proportion

Glioma is the cancer with the lowest WGD proportion, around 5%:

Cancer Panel - Non WGD Cohort

Glioma is highly enriched in Primary Samples, in >1muts subgroup and in composite mutation samples in general

Genome Instability

Glioma is one of the three cancers that shows an outlying signal in Genome Instability Pattern: while almost all cancers have a correlation between tp53 bi allelic state, Glioma, Pancreatic Cancer an d Melanoma do not show this GI difference.

Glioma shows an outlying pattern:

  • 0_HETLOSS is as high as >=1_LOSS
  • '>=1_cnLOH' is the lowest subgroup, while it is a likely bi-allelic subgroup like >=1_LOSS

WGD Part

Subgroup Proportion

We have already seen that the WGD proportion of Glioma was really low. Here we see that the WGD cohort size is very small in comparison with the Non-WGD Cohort size for Glioma.

Very High Genome Instability

In WGD cohort, Genome Instability median is above 70% for all cancer types.

Non WGD Part - Cancer Investigation

In this section, our goal is to find subcohorts that lead the signals observed. Here are the different subcohort we will create:

  • Hotspot Analysis: splitting on 273 / 248 / 175 / Missense / Truncated / In Frame
  • CCF Analysis
  • SNV/INDEL Analysis

Hotspot Analysis

In this section, we cut our cohort to only keep samples with exactly one TP53 mutation, for simplicity.

In [10]:
master_hotspot = master_no_wgd_cancer[master_no_wgd_cancer['tp53_count'] == 1]
In [13]:
get_hotspot_frac(df=master_hotspot,
                group_type=None,
                group=None)
Out[13]:
0 1 2
0 spot # frac
1 273 29 0.171
2 248 23 0.17
3 nan 10 0.34
4 175 9 0.203
5 193 8 0.1855
6 282 8 0.211
7 245 7 0.256
8 238 7 0.214
9 342 6 0.334
10 179 5 0.171

Entire Cohort

In [20]:
h = get_groupby(master_hotspot,'tp53_vc_group_1', 'count').sort_values(by='count', ascending=False)
display(h)

h = h.T
h = h[mutation_list]
fig = plt.figure(figsize=(6,1))
ax = plt.subplot()

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax, colormap="Accent")
ax.legend(['In Frame', 'Truncated', 'Missense', 'Hotspot 248','Hotspot 273','Hotspot 175', 'Other Hotspot'],loc='center left', bbox_to_anchor=(1.2, 0.5), fontsize=11)
ax.set_title('Mutation Type - {} - No WGD'.format(cancer), weight='bold', fontsize=18)

plt.show()
count
tp53_vc_group_1
missense 118
truncated 49
273 29
248 23
hotspot 19
in_frame 12
175 9
In [21]:
fig, ax = boxplot_sampletype(df=master_hotspot,
                  group='tp53_vc_group_1',
                  palette=mutation_palette,
                  order=mutation_list,
                  metrics='frac_genome_altered',
                  figsize=(6,10),
                  title='Fraction of Genome Altered - {}'.format(cancer),
                  xlim=[0,1])
plt.show()
  • Once more, it seems that 273 hotspot leads to lower GI
  • In Glioma, Truncated mutations seem to be more unstable than Missense

TP53 Residual Subgroups

In [22]:
print('Number of Bi Allelic samples (with 1 mut): ' + str(master_hotspot[master_hotspot['tp53_res_group'] == 'no_tp53_res'].shape[0]))
print('')
print('Number of  TP53 Residual samples (with 1 mut): ' + str(master_hotspot[master_hotspot['tp53_res_group'] == 'tp53_res'].shape[0]))

Number of Bi Allelic samples (with 1 mut): 182

Number of  TP53 Residual samples (with 1 mut): 63
In [23]:
total_df = []
for group in ['tp53_res', 'no_tp53_res']:
    h = get_groupby(master_hotspot[master_hotspot['tp53_res_group'] == group], 'tp53_vc_group_1', group).sort_values(by=group, ascending=False)
    total_df.append(h)
    
    h=h.T
    
    for mut in mutation_list:
        if mut not in h.columns:
            h[mut] = 0

    h = h[mutation_list]
    fig = plt.figure(figsize=(6,1))
    ax = plt.subplot()

    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)

    h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax, colormap="Accent")
    if group == 'tp53_res':
        ax.legend(['In Frame', 'Truncated', 'Missense', 'Hotspot 248','Hotspot 273','Hotspot 175', 'Other Hotspot'],loc='center left', bbox_to_anchor=(1.05, 0.5), fontsize=11)
    else: ax.get_legend().remove()
    ax.set_title('Mutation Type - {} - No WGD'.format(group), weight='bold', fontsize=18)

    plt.show()

display_side_by_side(total_df[0],total_df[1])
tp53_res
tp53_vc_group_1
missense 34
truncated 8
248 6
273 5
175 4
hotspot 4
in_frame 2
no_tp53_res
tp53_vc_group_1
missense 77
truncated 37
273 24
248 16
hotspot 13
in_frame 10
175 5
In [24]:
for group in ['tp53_res', 'no_tp53_res']:
    master_wt = master_hotspot[master_hotspot['tp53_res_group'] == group]

    fig, ax = boxplot_sampletype(df=master_wt,
                      group='tp53_vc_group_1',
                      palette=mutation_palette,
                      order=mutation_list,
                      metrics='frac_genome_altered',
                      figsize=(6,10),
                      title='Fraction of Genome Altered - No WGD - {} subgroup'.format(group),
                      xlim=[0,1])
    plt.show()

SNV / INDEL Analysis

In this section we compare SNV and INDEL mutations. As in the previous section, we cut the cohort to keep only samples with exactly 1 tp53 mutation.

# of Drivers / SNV Drivers / INDEL Drivers

In [25]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='driver_mutation_count',
                  figsize=(8,12),
                  title='Driver Mutation Count - TP53 Subroups - No WGD',
                  xlim=[-0.1,10])
plt.show()

display_side_by_side(get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='driver_mutation_count', 
               group_list=['1_WILD_TYPE', '0_HETLOSS']),
       
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='driver_mutation_count', 
               group_list=['1_WILD_TYPE', '>=1_LOSS']),
        
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='driver_mutation_count', 
               group_list=['>1muts', '>=1_LOSS']))
0 1 2
0 size driver_mutation_count
1 1_WILD_TYPE 63 2
2 0_HETLOSS 40 2
3 Statistics p-value
4 1.69166 0.0907104
0 1 2
0 size driver_mutation_count
1 1_WILD_TYPE 63 2
2 >=1_LOSS 74 2
3 Statistics p-value
4 1.38202 0.166965
0 1 2
0 size driver_mutation_count
1 >1muts 95 3
2 >=1_LOSS 74 2
3 Statistics p-value
4 2.30844 0.0209748
In [26]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='snv_driver_mutation_count',
                  figsize=(8,12),
                  title='SNV Driver Mutation Count - TP53 Subroups - No WGD',
                  xlim=[-0.1,15])
plt.show()

display_side_by_side(get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='snv_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '0_HETLOSS']),
       
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='snv_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '>=1_LOSS']),
        
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='snv_driver_mutation_count', 
               group_list=['>1muts', '>=1_LOSS']))
0 1 2
0 size snv_driver_mutation_count
1 1_WILD_TYPE 63 2
2 0_HETLOSS 40 2
3 Statistics p-value
4 2.13826 0.0324955
0 1 2
0 size snv_driver_mutation_count
1 1_WILD_TYPE 63 2
2 >=1_LOSS 74 2
3 Statistics p-value
4 2.60641 0.00914977
0 1 2
0 size snv_driver_mutation_count
1 >1muts 95 2
2 >=1_LOSS 74 2
3 Statistics p-value
4 2.09138 0.0364942
In [27]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='indel_driver_mutation_count',
                  figsize=(8,12),
                  title='INDEL Driver Mutation Count - TP53 Subroups - No WGD',
                  xlim=[-0.1,35])
plt.show()

display_side_by_side(get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '0_HETLOSS']),
       
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['1_WILD_TYPE', '>=1_LOSS']),
        
       get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='indel_driver_mutation_count', 
               group_list=['>1muts', '>=1_LOSS']))
0 1 2
0 size indel_driver_mutation_count
1 1_WILD_TYPE 63 0
2 0_HETLOSS 40 0
3 Statistics p-value
4 -0.17255 0.863006
0 1 2
0 size indel_driver_mutation_count
1 1_WILD_TYPE 63 0
2 >=1_LOSS 74 1
3 Statistics p-value
4 -1.52022 0.128455
0 1 2
0 size indel_driver_mutation_count
1 >1muts 95 1
2 >=1_LOSS 74 1
3 Statistics p-value
4 0.930029 0.352356

Here, one major information:

  • In Glioma, there are very few numbers of Co-Drivers, INDEL Co-Drivers nor SNV Co-Drivers.
    • Maybe TP53 mutations in Glioma are very pervasvie and do not need more drivers to expand

Genome Instability

The idea here is to see if we have differences in Fraction of Genome Altered if we cut our Cancer cohort on the number of drivers per sample.

Do we have more instability with more INDEL Driver Mutations within the same subgroup?

1_WILD_TYPE Subgroup

In [28]:
master_no_wgd_cancer_wt = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '1_WILD_TYPE']

thr=1

def get_driver_groups(x):
    if x.indel_driver_mutation_count > thr:
        return 'High Co-Driver Count'
    if x.indel_driver_mutation_count <= thr:
        return 'Low Co-Driver Count'
    

master_no_wgd_cancer_wt['co_driver_group'] = master_no_wgd_cancer_wt.apply(get_driver_groups, axis=1)
In [29]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_wt,
                  group='co_driver_group',
                  palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
                  order=['High Co-Driver Count', 'Low Co-Driver Count'],
                  metrics='frac_genome_altered',
                  figsize=(4,10),
                  title='Fraction of Genome Altered - 1_WILD_TYPE subgroup - Co Driver Count (thr={}) - {}'.format(thr,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_no_wgd_cancer_wt,
               group='co_driver_group',
               metrics='frac_genome_altered',
               group_list=['High Co-Driver Count', 'Low Co-Driver Count'])
Out[29]:
0 1 2
0 size frac_genome_altered
1 High Co-Driver Count 6 0.3095
2 Low Co-Driver Count 57 0.203
3 Statistics p-value
4 2.59903 0.00934888

0_HETLOSS

In [30]:
master_no_wgd_cancer_het = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '0_HETLOSS']

thr=1

def get_driver_groups(x):
    if x.indel_driver_mutation_count > thr:
        return 'High Co-Driver Count'
    if x.indel_driver_mutation_count <= thr:
        return 'Low Co-Driver Count'
    

master_no_wgd_cancer_het['co_driver_group'] = master_no_wgd_cancer_het.apply(get_driver_groups, axis=1)
In [31]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer_het,
                  group='co_driver_group',
                  palette={'High Co-Driver Count': '#FF9900' , 'Low Co-Driver Count': '#146EB4'},
                  order=['High Co-Driver Count', 'Low Co-Driver Count'],
                  metrics='frac_genome_altered',
                  figsize=(4,10),
                  title='Fraction of Genome Altered - 0_HETLOSS subgroup - Co Driver Count (thr={}) - {}'.format(thr,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_no_wgd_cancer_het,
               group='co_driver_group',
               metrics='frac_genome_altered',
               group_list=['High Co-Driver Count', 'Low Co-Driver Count'])
Out[31]:
0 1 2
0 size frac_genome_altered
1 High Co-Driver Count 4 0.258
2 Low Co-Driver Count 36 0.2815
3 Statistics p-value
4 0.13525 0.892414

Co Driver Analysis

In [6]:
codrivers_glioma = get_major_codrivers(master=master_no_wgd_cancer,
                    maf=maf_cohort_nowgd,
                    head=15)

codrivers_glioma_tp53 = get_major_codrivers(master=master_no_wgd_cancer[master_no_wgd_cancer['tp53_count'] >= 1],
                    maf=maf_cohort_nowgd,
                    head=15)
In [7]:
co_drivers = pd.merge(codrivers_glioma, codrivers_glioma_tp53, on='Hugo_Symbol')
co_drivers.columns = ['cancer', 'cancer_tp53']
In [163]:
co_drivers['ratio'] = co_drivers.apply(lambda x: 100*round(x.cancer_tp53/x.cancer, 4) , axis=1)
co_drivers = co_drivers.sort_values(by='ratio', ascending=False)
In [164]:
co_drivers
Out[164]:
cancer cancer_tp53 ratio
Hugo_Symbol
ATRX 144 126 87.50
RB1 85 58 68.24
IDH1 285 152 53.33
ARID1A 35 14 40.00
PIK3R1 110 33 30.00
PTEN 302 90 29.80
PIK3CA 134 37 27.61
NOTCH1 42 11 26.19
NF1 203 52 25.62
PTPN11 59 13 22.03
TERT 915 184 20.11
EGFR 213 31 14.55
CIC 149 12 8.05
In [166]:
labels = []
for element in co_drivers.index.tolist():
    labels.append(element + ' ('+ str(int(co_drivers.loc[element]['cancer']))+')')

ax = sns.barplot(y=co_drivers.index, x='ratio',data=co_drivers[['ratio']], color='#7F8C8D', saturation=.2)
ax.set_yticklabels(labels)
ax.set_title('Co-Drivers Enrichment in TP53 State')
Out[166]:
Text(0.5, 1.0, 'Co-Drivers Enrichment in TP53 State')
In [321]:
codrivers_glioma
Out[321]:
count
Hugo_Symbol
TERT 915
PTEN 302
IDH1 285
EGFR 213
NF1 203
CIC 149
ATRX 144
PIK3CA 134
PIK3R1 110
RB1 85
PTPN11 59
NOTCH1 42
FUBP1 38
ARID1A 35
SETD2 33
In [378]:
labels = []
codrivers_glioma = get_major_codrivers(master=master_no_wgd_cancer,
                                       maf=maf_cohort_nowgd,
                                       head=15,
                                       tp53=True)

codrivers_glioma['proportion'] = codrivers_glioma.apply(lambda x: 100* round(x['count'] / codrivers_glioma.sum().values[0], 4), axis=1)

for element in codrivers_glioma.head(15).index.tolist():
    labels.append(element + ' ('+ str(int(codrivers_glioma.loc[element]['count']))+')')

ax = sns.barplot(y=codrivers_glioma.head(15).index, x='proportion',data=codrivers_glioma.head(15)[['proportion']], color='#7F8C8D', saturation=.2)
ax.set_yticklabels(labels)
ax.set_title('Drivers Frequency in {}'.format(cancer))
Out[378]:
Text(0.5, 1.0, 'Drivers Frequency in Glioma')

Co-Drivers per subgroup

In [379]:
co_drivers_res = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_res_group',
                                                group_1='tp53_res',
                                                group_2='no_tp53_res')
co_drivers_res
Out[379]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
TERT 99 -22.30 108 14.29
PTEN 37 -8.33 58 7.67
NF1 26 -5.86 31 4.10
EGFR 23 -5.18 10 1.32
PIK3CA 21 -4.73 21 2.78
IDH1 17 -3.83 130 17.20
PIK3R1 14 -3.15 21 2.78
RB1 11 -2.48 50 6.61
CIC 11 -2.48 2 0.26
PTPN11 9 -2.03 5 0.66
ATM 7 -1.58 2 0.26
SETD2 7 -1.58 7 0.93
KMT2D 6 -1.35 4 0.53
FUBP1 6 -1.35 1 0.13
ATRX 6 -1.35 114 15.08
BRAF 5 -1.13 1 0.13
ARID1B 4 -0.90 2 0.26
ARID1A 4 -0.90 11 1.46
NOTCH1 3 -0.68 8 1.06
MSH6 3 -0.68 5 0.66
MSH2 3 -0.68 1 0.13
MLH1 3 -0.68 2 0.26
MGA 3 -0.68 2 0.26
KRAS 3 -0.68 4 0.53
STAG2 3 -0.68 6 0.79
BRCA2 3 -0.68 2 0.26
ARID2 3 -0.68 11 1.46
BCOR 3 -0.68 5 0.66
MAP3K1 2 -0.45 1 0.13
TSC2 2 -0.45 2 0.26
TET2 2 -0.45 2 0.26
NOTCH2 2 -0.45 2 0.26
NOTCH3 2 -0.45 1 0.13
NSD1 2 -0.45 1 0.13
SMARCB1 2 -0.45 1 0.13
MED12 2 -0.45 4 0.53
FBXW7 2 -0.45 3 0.40
KDM5C 2 -0.45 1 0.13
FAT1 2 -0.45 1 0.13
KMT2C 1 -0.23 1 0.13
DICER1 1 -0.23 1 0.13
PTPRD 1 -0.23 1 0.13
PTPRS 1 -0.23 2 0.26
CDKN2A 1 -0.23 6 0.79
CDKN1B 1 -0.23 2 0.26
PTCH1 1 -0.23 1 0.13
SMARCA4 1 -0.23 4 0.53
CBL 1 -0.23 3 0.40
BRIP1 1 -0.23 1 0.13
ATR 1 -0.23 3 0.40
TGFBR2 1 -0.23 1 0.13
APC 1 -0.23 1 0.13
DNMT3A 1 -0.23 7 0.93
MYCN 1 -0.23 2 0.26
MEN1 1 -0.23 1 0.13
FLT3 1 -0.23 3 0.40
MTOR 1 -0.23 1 0.13
NOTCH4 1 -0.23 1 0.13
ETV6 1 -0.23 1 0.13
PDGFRA 1 -0.23 5 0.66
In [396]:
fig=plt.figure(figsize=(7,7))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_res[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = ['#2ECC71','#1E8449'])
ax.legend(['TP53 Residual', 'No TP53 Residual'], fontsize=10)
ax.set_title('Co-Drivers Proportion per TP53 State', fontsize=14)

plt.yticks(fontsize=10)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [25, 20, 15, 10, 5, 0, 5, 10, 15, 20]
ax.set_xticklabels(a, fontsize=10)
plt.grid(b=None)

plt.show()
In [9]:
co_drivers_cnloh_loss = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_group',
                                                group_1='>=1_cnLOH',
                                                group_2='>=1_LOSS')
co_drivers_cnloh_loss
Out[9]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
IDH1 80 -23.46 4 2.20
ATRX 68 -19.94 6 3.30
TERT 37 -10.85 48 26.37
PTEN 18 -5.28 29 15.93
RB1 16 -4.69 25 13.74
PIK3CA 13 -3.81 2 1.10
NF1 9 -2.64 13 7.14
PIK3R1 9 -2.64 8 4.40
H3F3A 6 -1.76 4 2.20
ARID2 6 -1.76 1 0.55
EGFR 5 -1.47 2 1.10
ARID1A 3 -0.88 2 1.10
CDKN2A 3 -0.88 2 1.10
DNMT3A 3 -0.88 2 1.10
PTPN11 3 -0.88 1 0.55
FBXW7 2 -0.59 1 0.55
MSH6 2 -0.59 1 0.55
FLT3 2 -0.59 1 0.55
STAG2 1 -0.29 3 1.65
TET2 1 -0.29 1 0.55
PBRM1 1 -0.29 1 0.55
MGA 1 -0.29 1 0.55
ATR 1 -0.29 1 0.55
EPCAM 1 -0.29 1 0.55
FGFR1 1 -0.29 1 0.55
HNF1A 1 -0.29 1 0.55
KMT2D 1 -0.29 2 1.10
In [10]:
labels = []
for element in co_drivers_cnloh_loss.head(10).index.tolist():
    labels.append(element + ' ('+ str(int(co_drivers_cnloh_loss.loc[element]['count_x']))+')')

ax = sns.barplot(y=co_drivers_cnloh_loss.head(10).index, x='proportion_1',data=co_drivers_cnloh_loss.head(10)[['proportion_1']], color='#7F8C8D', saturation=.2)
ax.set_yticklabels(labels)
ax.set_title('Co-Drivers Frequency in {} - {}'.format(subgroup, cancer))

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-10-ef47ed169b5d> in <module>
      5 ax = sns.barplot(y=co_drivers_cnloh_loss.head(10).index, x='proportion_1',data=co_drivers_cnloh_loss.head(10)[['proportion_1']], color='#7F8C8D', saturation=.2)
      6 ax.set_yticklabels(labels)
----> 7 ax.set_title('Co-Drivers Frequency in {} - {}'.format(subgroup, cancer))

NameError: name 'subgroup' is not defined
In [22]:
fig=plt.figure(figsize=(7,7))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_cnloh_loss[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[4],mc[0]])
ax.legend(['>=1_cnLOH', '>=1_LOSS'], fontsize=5)
ax.set_title('Co-Drivers Proportion per TP53 State')

plt.yticks(fontsize=7)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [30, 20, 10, 0, 10, 20, 30] 
ax.set_xticklabels(a, fontsize=8)
plt.grid(b=None)

plt.show()

Here we see two different strong signals:

  • In >=1_cnLOH, enrichment in IDH1 and ATRX and depletion in TERT
  • In >=1_LOSS, enrichment in TERT, but alos in PTEN and RB1
In [12]:
co_drivers_losses = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_group',
                                                group_1='0_HETLOSS',
                                                group_2='>=1_LOSS')
co_drivers_losses
Out[12]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
TERT 26 -27.37 48 26.37
PTEN 11 -11.58 29 15.93
NF1 8 -8.42 13 7.14
RB1 6 -6.32 25 13.74
PIK3CA 6 -6.32 2 1.10
EGFR 4 -4.21 2 1.10
IDH1 3 -3.16 4 2.20
STAG2 2 -2.11 3 1.65
PTPN11 2 -2.11 1 0.55
PIK3R1 2 -2.11 8 4.40
APC 1 -1.05 1 0.55
KMT2D 1 -1.05 2 1.10
FBXW7 1 -1.05 1 0.55
CBL 1 -1.05 1 0.55
ATRX 1 -1.05 6 3.30
In [13]:
fig=plt.figure(figsize=(5,5))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_losses[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[5],mc[0]])
ax.legend(['0_HETLOSS', '>=1_LOSS'], fontsize=5)
ax.set_title('Co-Drivers Proportion per TP53 State')

plt.yticks(fontsize=7)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [40, 30, 20, 10, 0, 10, 20, 30]
ax.set_xticklabels(a, fontsize=8)
plt.grid(b=None)

plt.show()

* Same enrichment in TERT and PTEN

In [331]:
co_drivers_mult_cnloh = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_group',
                                                group_1='>1muts',
                                                group_2='>=1_cnLOH')
co_drivers_mult_cnloh
Out[331]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
IDH1 51 -13.53 80 23.46
ATRX 44 -11.67 68 19.94
TERT 39 -10.34 37 10.85
PTEN 20 -5.31 18 5.28
NF1 20 -5.31 9 2.64
RB1 12 -3.18 16 4.69
PIK3CA 10 -2.65 13 3.81
PIK3R1 9 -2.39 9 2.64
EGFR 8 -2.12 5 1.47
ARID1A 8 -2.12 3 0.88
NOTCH1 6 -1.59 5 1.47
ARID2 6 -1.59 6 1.76
PTPN11 5 -1.33 3 0.88
BCOR 5 -1.33 2 0.59
MSH6 5 -1.33 2 0.59
KMT2D 4 -1.06 1 0.29
CIC 4 -1.06 1 0.29
SETD2 3 -0.80 3 0.88
H3F3A 3 -0.80 6 1.76
KRAS 3 -0.80 1 0.29
SMARCA4 3 -0.80 2 0.59
MED12 3 -0.80 1 0.29
DNMT3A 3 -0.80 3 0.88
CDKN2A 3 -0.80 3 0.88
PDGFRA 3 -0.80 2 0.59
ATR 2 -0.53 1 0.29
STAG2 2 -0.53 1 0.29
TET2 1 -0.27 1 0.29
SMARCB1 1 -0.27 1 0.29
PBRM1 1 -0.27 1 0.29
NOTCH4 1 -0.27 1 0.29
ARID1B 1 -0.27 2 0.59
BRAF 1 -0.27 1 0.29
CDK4 1 -0.27 1 0.29
EIF1AX 1 -0.27 1 0.29
INPPL1 1 -0.27 2 0.59
KDM5C 1 -0.27 1 0.29
KMT2A 1 -0.27 1 0.29
MGA 1 -0.27 1 0.29
MYCN 1 -0.27 1 0.29
In [341]:
fig=plt.figure(figsize=(5,5))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_mult_cnloh[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[3],mc[4]])
ax.legend(['>1muts', '>=1_cnLOH'], fontsize=5)
ax.set_title('Co-Drivers Proportion per TP53 State')

plt.yticks(fontsize=7)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [20, 15, 10, 5, 0, 5, 10, 15, 20, 25, 30]
ax.set_xticklabels(a, fontsize=8)
plt.grid(b=None)

plt.show()

* Same pattern of co-drivers distribution

In [337]:
co_drivers_wt_loss = create_co_drivers_table(master=master_no_wgd_cancer, 
                                                group_type='tp53_group',
                                                group_1='1_WILD_TYPE',
                                                group_2='>=1_LOSS')
co_drivers_wt_loss
Out[337]:
count_x proportion_1 count_y proportion_2
Hugo_Symbol
TERT 57 -25.45 48 26.37
PTEN 20 -8.93 29 15.93
EGFR 13 -5.80 2 1.10
PIK3CA 10 -4.46 2 1.10
IDH1 10 -4.46 4 2.20
NF1 10 -4.46 13 7.14
PIK3R1 7 -3.12 8 4.40
RB1 5 -2.23 25 13.74
ATM 5 -2.23 1 0.55
ATRX 4 -1.79 6 3.30
PTPN11 4 -1.79 1 0.55
MGA 2 -0.89 1 0.55
KMT2D 2 -0.89 2 1.10
ARID2 2 -0.89 1 0.55
TSC2 1 -0.45 2 1.10
TET2 1 -0.45 1 0.55
STAG2 1 -0.45 3 1.65
FAT1 1 -0.45 1 0.55
FBXW7 1 -0.45 1 0.55
FLT3 1 -0.45 1 0.55
KMT2C 1 -0.45 1 0.55
ARID1A 1 -0.45 2 1.10
In [340]:
fig=plt.figure(figsize=(5,5))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_wt_loss[['proportion_1', 'proportion_2']].head(10)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = [mc[2],mc[0]])
ax.legend(['1_WILD_TYPE', '>=1_LOSS'], fontsize=5)
ax.set_title('Co-Drivers Proportion per TP53 State')

plt.yticks(fontsize=7)
ax.set_ylabel('')
a=ax.get_xticks().tolist()

a = [30, 20, 10, 0, 10, 20, 30]
ax.set_xticklabels(a, fontsize=8)
plt.grid(b=None)

plt.show()

Comparison with WGD Cohort (WGD - TP53 - LOH)

In [14]:
def get_major_codrivers(master: pd.DataFrame, maf: pd.DataFrame, head:int = 10):
    samples = master.Tumor_Id.tolist()
    maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True][maf['Hugo_Symbol'] != 'TP53']
    h = pd.DataFrame(maf_filtered[['Hugo_Symbol']].groupby(['Hugo_Symbol']).size())
    h.columns = ['count']
    h = h.sort_values(by='count', ascending=False).head(head)
    
    return(h)

def create_co_drivers_table_wgd(master_1: pd.DataFrame, master_2: pd.DataFrame, group_type:str,  group_1: str):
    master_group_1 = master_1[master_1[group_type] == group_1]
    co_drivers_group_1 = get_major_codrivers(master=master_group_1,
                        maf=maf_cohort_nowgd,
                       head=100)
    co_drivers_group_1['proportion_1'] = co_drivers_group_1.apply(lambda x: 100* round(x['count'] / co_drivers_group_1.sum().values[0], 4), axis=1)
    
    master_group_2 = master_2[master_2['tp53_count'] >=1][master_2['tp53_loh_status'] == True]
    co_drivers_group_2 = get_major_codrivers(master=master_group_2,
                        maf=maf_cohort_wgd,
                       head=100)
    co_drivers_group_2['proportion_2'] = co_drivers_group_2.apply(lambda x: 100* round(x['count'] / co_drivers_group_2.sum().values[0], 4), axis=1)
    
    co_drivers_groups = pd.merge(co_drivers_group_2, co_drivers_group_1, on='Hugo_Symbol')
    co_drivers_groups['proportion_2'] = - co_drivers_groups['proportion_2']
    
    return co_drivers_groups
In [362]:
co_drivers_wgd_loss = create_co_drivers_table_wgd(master_1=master_no_wgd_cancer,
                            master_2=master_wgd_cancer,
                            group_type='tp53_group',
                            group_1='>=1_LOSS')
co_drivers_wgd_loss
Out[362]:
count_x proportion_2 count_y proportion_1
Hugo_Symbol
PTEN 15 -9.62 29 15.93
TERT 14 -8.97 48 26.37
RB1 12 -7.69 25 13.74
IDH1 11 -7.05 4 2.20
ATRX 11 -7.05 6 3.30
PIK3CA 9 -5.77 2 1.10
NF1 9 -5.77 13 7.14
PIK3R1 9 -5.77 8 4.40
H3F3A 3 -1.92 4 2.20
TSC2 3 -1.92 2 1.10
MSH6 3 -1.92 1 0.55
EGFR 2 -1.28 2 1.10
KMT2C 2 -1.28 1 0.55
PTPN11 1 -0.64 1 0.55
NOTCH2 1 -0.64 1 0.55
ATM 1 -0.64 1 0.55
DNMT3A 1 -0.64 2 1.10
FAT1 1 -0.64 1 0.55
KMT2D 1 -0.64 2 1.10
MSH2 1 -0.64 1 0.55
In [366]:
fig=plt.figure(figsize=(5,5))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)

co_drivers_wgd_loss[['proportion_2', 'proportion_1']].head(15)[::-1].plot.barh(stacked=True, ax=ax, width=1, color = ['#7F8C8D',mc[0]])
ax.legend(['WGD - TP53 - LOH', '>=1_LOSS'], fontsize=5)
ax.set_title('Co-Drivers Proportion per TP53 State')

plt.yticks(fontsize=7)
ax.set_ylabel('')
a=ax.get_xticks().tolist()
a = [-15, -10, -5, 0, 5, 10, 15, 20, 25, 30]
ax.set_xticklabels(a, fontsize=8)
plt.grid(b=None)

plt.show()

Genome Instability Associated

In [15]:
def get_master_codrivers(master: pd.DataFrame, maf: pd.DataFrame, symbol: str):
    samples = master.Tumor_Id.tolist()
    samples_final = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['Hugo_Symbol'] == symbol].Tumor_Sample_Barcode.tolist()

    master_filtered = master[master.Tumor_Id.isin(samples_final)]
    
    return master_filtered

TP53 Res

In [16]:
master_no_wgd_cancer_res = master_no_wgd_cancer[master_no_wgd_cancer['tp53_res_group'] == 'no_tp53_res']
master_IDH1 = get_master_codrivers(master=master_no_wgd_cancer_res,
                                   maf=maf_cohort_nowgd,
                                   symbol='IDH1')

master_ATRX = get_master_codrivers(master=master_no_wgd_cancer_res,
                                   maf=maf_cohort_nowgd,
                                   symbol='ATRX')

master_TERT = get_master_codrivers(master=master_no_wgd_cancer_res,
                                   maf=maf_cohort_nowgd,
                                   symbol='TERT')

master_no_wgd_cancer_res['data'] = 'no_tp53_res'
master_IDH1['data'] = 'IDH1'
master_ATRX['data'] = 'ATRX'
master_TERT['data'] = 'TERT'

masters = [master_no_wgd_cancer_res, master_IDH1, master_ATRX, master_TERT]
allMasters = pd.concat(masters)
In [17]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - No TP53 Residual subgroup')
ax.set_xlabel('')
Out[17]:
Text(0.5, 0, '')

>=1_cnLOH

In [368]:
master_no_wgd_cancer_cnloh = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>=1_cnLOH']
master_IDH1 = get_master_codrivers(master=master_no_wgd_cancer_cnloh,
                                   maf=maf_cohort_nowgd,
                                   symbol='IDH1')

master_ATRX = get_master_codrivers(master=master_no_wgd_cancer_cnloh,
                                   maf=maf_cohort_nowgd,
                                   symbol='ATRX')

master_TERT = get_master_codrivers(master=master_no_wgd_cancer_cnloh,
                                   maf=maf_cohort_nowgd,
                                   symbol='TERT')

master_no_wgd_cancer_cnloh['data'] = '>=1_cnLOH'
master_IDH1['data'] = 'IDH1'
master_ATRX['data'] = 'ATRX'
master_TERT['data'] = 'TERT'

masters = [master_no_wgd_cancer_cnloh, master_IDH1, master_ATRX, master_TERT]
allMasters = pd.concat(masters)
In [369]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - >=1_cnLOH')
ax.set_xlabel('')
Out[369]:
Text(0.5, 0, '')

>=1_LOSS

In [397]:
master_no_wgd_cancer_loss = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>=1_LOSS']
master_TERT = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='TERT')

master_PTEN = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='PTEN')

master_RB1 = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='RB1')

master_no_wgd_cancer_loss['data'] = '>=1_LOSS'
master_TERT['data'] = 'TERT'
master_PTEN['data'] = 'PTEN'
master_RB1['data'] = 'RB1'


masters = [master_no_wgd_cancer_loss, master_TERT, master_PTEN, master_RB1]
allMasters = pd.concat(masters)
In [398]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - >=1_LOSS')
ax.set_xlabel('')
Out[398]:
Text(0.5, 0, '')

0_HETLOSS

In [19]:
master_no_wgd_cancer_loss = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '0_HETLOSS']
master_TERT = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='TERT')

master_PTEN = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='PTEN')

master_RB1 = get_master_codrivers(master=master_no_wgd_cancer_loss,
                                   maf=maf_cohort_nowgd,
                                   symbol='RB1')

master_no_wgd_cancer_loss['data'] = '0_HETLOSS'
master_TERT['data'] = 'TERT'
master_PTEN['data'] = 'PTEN'
master_RB1['data'] = 'RB1'


masters = [master_no_wgd_cancer_loss, master_TERT, master_PTEN, master_RB1]
allMasters = pd.concat(masters)
In [20]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - 0_HETLOSS')
ax.set_xlabel('')
Out[20]:
Text(0.5, 0, '')

>1muts

In [1]:
master_no_wgd_cancer_muts = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>1muts']
master_IDH1 = get_master_codrivers(master=master_no_wgd_cancer_muts,
                                   maf=maf_cohort_nowgd,
                                   symbol='IDH1')

master_ATRX = get_master_codrivers(master=master_no_wgd_cancer_muts,
                                   maf=maf_cohort_nowgd,
                                   symbol='ATRX')

master_TERT = get_master_codrivers(master=master_no_wgd_cancer_muts,
                                   maf=maf_cohort_nowgd,
                                   symbol='TERT')

master_no_wgd_cancer_muts['data'] = '>1muts'
master_IDH1['data'] = 'IDH1'
master_ATRX['data'] = 'ATRX'
master_TERT['data'] = 'TERT'

masters = [master_no_wgd_cancer_muts, master_IDH1, master_ATRX, master_TERT]
allMasters = pd.concat(masters)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-5d0623bf080d> in <module>
----> 1 master_no_wgd_cancer_muts = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '>1muts']
      2 master_IDH1 = get_master_codrivers(master=master_no_wgd_cancer_muts,
      3                                    maf=maf_cohort_nowgd,
      4                                    symbol='IDH1')
      5 

NameError: name 'master_no_wgd_cancer' is not defined
In [344]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - >1muts')
ax.set_xlabel('')
Out[344]:
Text(0.5, 0, '')

1_WILD_TYPE

In [374]:
master_no_wgd_cancer_wt = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == '1_WILD_TYPE']
master_EGFR = get_master_codrivers(master=master_no_wgd_cancer_wt,
                                   maf=maf_cohort_nowgd,
                                   symbol='EGFR')

master_PTEN = get_master_codrivers(master=master_no_wgd_cancer_wt,
                                   maf=maf_cohort_nowgd,
                                   symbol='PTEN')

master_TERT = get_master_codrivers(master=master_no_wgd_cancer_wt,
                                   maf=maf_cohort_nowgd,
                                   symbol='TERT')

master_no_wgd_cancer_wt['data'] = '1_WT'
master_EGFR['data'] = 'EGFR'
master_PTEN['data'] = 'PTEN'
master_TERT['data'] = 'TERT'

masters = [master_no_wgd_cancer_wt, master_PTEN, master_EGFR, master_TERT]
allMasters = pd.concat(masters)
In [375]:
fig=plt.figure(figsize=(5,10))
ax = plt.subplot2grid(shape=(2,1), loc=(0,0), colspan=1)
allMasters[['frac_genome_altered', 'data']].boxplot(by="data", ax=ax)
ax.set_title('Fraction of Genome Altered - 1_WILD_TYPE')
ax.set_xlabel('')
Out[375]:
Text(0.5, 0, '')

CCF / VAF Analysis

Same here we take only samples with exactly 1 tp53 mutation (master_hotspot).

We have to define groups for CCF to see if there are differences between those groups. To have an idea of the CCF distribution we show here the distribution coming from the cancer_panel.

We see that our tp53_ccf distribution is very high for all subgroups except for 1_WILD_TYPE.

It will be hard to cut the cohort based on the CCF. Let's try and see the size of the subcohorts:

In [32]:
master_ccf = master_no_wgd_cancer[(master_no_wgd_cancer['tp53_count'] == 1) | (master_no_wgd_cancer['tp53_group'] == '0_HETLOSS')]
In [39]:
thr_ccf_1 = 0.90 ; thr_ccf_2 = 0.95

def ccf_subgroup(x):
    if x.tp53_ccf_1 <= thr_ccf_1: return 'low'
    elif x.tp53_ccf_1 <= thr_ccf_2: return 'medium'
    elif x.tp53_ccf_1 > thr_ccf_2: return 'high'

master_ccf['ccf_group'] = master_ccf.apply(ccf_subgroup, axis=1)
get_groupby(master_ccf, 'ccf_group', 'count')
Out[39]:
count
ccf_group
high 188
low 47
medium 21
In [40]:
thr_vaf_1 = 0.3 ; thr_vaf_2 = 0.4

def vaf_subgroup(x):
    if x.tp53_vaf_1 <= thr_vaf_1: return 'low'
    elif x.tp53_vaf_1 <= thr_vaf_2: return 'medium'
    elif x.tp53_vaf_1 > thr_vaf_2: return 'high'

master_ccf['vaf_group'] = master_ccf.apply(vaf_subgroup, axis=1)       
get_groupby(master_ccf, 'vaf_group', 'count')
Out[40]:
count
vaf_group
high 173
low 56
medium 30

VAF Analysis

No VAF Cut

In [41]:
fig, ax = boxplot_sampletype(df=master_ccf,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_ccf,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['0_HETLOSS', '1_WILD_TYPE'])
Out[41]:
0 1 2
0 size frac_genome_altered
1 0_HETLOSS 40 0.2815
2 1_WILD_TYPE 63 0.215
3 Statistics p-value
4 3.45099 0.000558532

Low VAF

In [42]:
master_low = master_ccf[(master_ccf['vaf_group'] == 'low') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_low,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - VAF < {} - {}'.format(thr_vaf_1,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_low,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '>=1_LOSS'])
Out[42]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 36 0.219
2 >=1_LOSS 13 0.31
3 Statistics p-value
4 -2.51366 0.0119487

Medium VAF

In [43]:
master_med = master_ccf[(master_ccf['vaf_group'] == 'medium') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_med,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - {} < VAF < {} - {}'.format(thr_vaf_1,thr_vaf_2,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_med,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[43]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 16 0.2045
2 0_HETLOSS 40 0.2815
3 Statistics p-value
4 -2.5301 0.0114031

High VAF

In [44]:
master_high = master_ccf[(master_ccf['vaf_group'] == 'high') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_high,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - VAF > {} - {}'.format(thr_vaf_2,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_high,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[44]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 11 0.211
2 0_HETLOSS 40 0.2815
3 Statistics p-value
4 -1.85502 0.063594

CCF Analysis

No CCF Cut

In [45]:
fig, ax = boxplot_sampletype(df=master_ccf,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_ccf,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['0_HETLOSS', '1_WILD_TYPE'])
Out[45]:
0 1 2
0 size frac_genome_altered
1 0_HETLOSS 40 0.2815
2 1_WILD_TYPE 63 0.215
3 Statistics p-value
4 3.45099 0.000558532

Low CCF

In [46]:
master_low = master_ccf[(master_ccf['ccf_group'] == 'low') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_low,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE','0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - CCF < {} - {}'.format(thr_ccf_1,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_low,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[46]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 27 0.191
2 0_HETLOSS 40 0.2815
3 Statistics p-value
4 -3.707 0.000209731

Medium CCF

In [47]:
master_med = master_ccf[(master_ccf['ccf_group'] == 'medium') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_med,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - {} < CCF < {} - {}'.format(thr_ccf_1,thr_ccf_2,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_med,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[47]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 6 0.1515
2 0_HETLOSS 40 0.2815
3 Statistics p-value
4 -2.1853 0.0288669

High CCF

In [48]:
master_high = master_ccf[(master_ccf['ccf_group'] == 'high') | (master_ccf['tp53_group'] == '0_HETLOSS')]

fig, ax = boxplot_sampletype(df=master_high,
                  group='tp53_group',
                  palette=palette,
                  order=['1_WILD_TYPE', '0_HETLOSS', '>=1_LOSS', '>=1_cnLOH'],
                  metrics='frac_genome_altered',
                  figsize=(5,10),
                  title='Fraction of Genome Altered - CCF > {} - {}'.format(thr_ccf_2,cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_high,
               group='tp53_group',
               metrics='frac_genome_altered',
               group_list=['1_WILD_TYPE', '0_HETLOSS'])
Out[48]:
0 1 2
0 size frac_genome_altered
1 1_WILD_TYPE 30 0.2335
2 0_HETLOSS 40 0.2815
3 Statistics p-value
4 -1.81578 0.0694047

Splitting on the different level of CCF / VAF

In [ ]:
fig, ax = boxplot_sampletype(df=master_hotspot,
                  group='vaf_group',
                  palette={'low': tab10[0] , 'medium': tab10[1], 'high':tab10[2]},
                  order=['low', 'medium', 'high'],
                  metrics='frac_genome_altered',
                  figsize=(3,10),
                  title='Fraction of Genome Altered - VAF levels - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_hotspot,
               group='vaf_group',
               metrics='frac_genome_altered',
               group_list=['low', 'medium'])
In [ ]:
fig, ax = boxplot_sampletype(df=master_hotspot,
                  group='ccf_group',
                  palette={'low': tab10[0] , 'medium': tab10[1], 'high':tab10[2]},
                  order=['low', 'medium', 'high'],
                  metrics='frac_genome_altered',
                  figsize=(3,10),
                  title='Fraction of Genome Altered - CCF levels - {}'.format(cancer),
                  xlim=[0,1])
plt.show()

get_statistics(df=master_hotspot,
               group='ccf_group',
               metrics='frac_genome_altered',
               group_list=['low', 'medium'])

>=1_cnLOH Subgroup investigation

Something weird happens with >=1_cnLOH. Indeed, this subgroup has a very low Fraction of Genome Altered, but in the meantime it is the subgroup with the lowest median Age. This is counterintuitive ... Let's try to see which part of >=1_cnLOH is leading the signal

Hotspots

In [63]:
master_hotspot_cnloh = master_hotspot[master_hotspot['tp53_group'] == '>=1_cnLOH']
In [64]:
get_hotspot_frac(df=master_hotspot_cnloh,
                group_type=None,
                group=None)
Out[64]:
0 1 2
0 spot # frac
1 273 22 0.1345
2 248 10 0.1195
3 193 6 0.1855
4 282 5 0.213
5 175 4 0.1375
6 nan 3 0.343
7 255 3 0.431
8 176 3 0.156
9 157 3 0.179
10 266 3 0.058
In [66]:
h = get_groupby(master_hotspot_cnloh,'tp53_vc_group_1', 'count').sort_values(by='count', ascending=False)
display(h)

h = h.T
h = h[mutation_list]
fig = plt.figure(figsize=(6,1))
ax = plt.subplot()

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax, colormap="Accent")
ax.legend(['In Frame', 'Truncated', 'Missense', 'Hotspot 248','Hotspot 273','Hotspot 175', 'Other Hotspot'],loc='center left', bbox_to_anchor=(1.2, 0.5), fontsize=11)
ax.set_title('Mutation Type - {} - >=1_cnLOH - No WGD'.format(cancer), weight='bold', fontsize=18)

plt.show()
count
tp53_vc_group_1
missense 49
273 22
truncated 18
248 10
in_frame 9
hotspot 8
175 4

In comparison with the entire Glioma cohort, we have a clear enrichment in Hotspot 273 (22 out of 29 are in >=1_cnLOH subgroup)

  • 273 / 248 are associated with low Genome Instability
  • 193 / 282 with higher GI
In [68]:
fig, ax = boxplot_sampletype(df=master_hotspot_cnloh,
                  group='tp53_vc_group_1',
                  palette=mutation_palette,
                  order=mutation_list,
                  metrics='frac_genome_altered',
                  figsize=(6,10),
                  title='Fraction of Genome Altered - {} - >=1_cnLOH'.format(cancer),
                  xlim=[0,1])
plt.show()

Main Co-Drivers

In this section, we want to see what are the main co-drivers in >=1_cnLOH subgroup.

In [73]:
h = master_hotspot_cnloh.driver_gene_count.mean()
print('Each >=1_cnLOH sample as an average of ' + str(h) + ' co-driver genes')

Each >=1_cnLOH sample as an average of 2.55 co-driver genes
In [100]:
def get_major_codrivers(master: pd.DataFrame, maf: pd.DataFrame, head:int = 10):
    samples = master.Tumor_Id.tolist()
    maf_filtered = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['driver'] == True][maf['Hugo_Symbol'] != 'TP53']
    h = pd.DataFrame(maf_filtered[['Hugo_Symbol']].groupby(['Hugo_Symbol']).size())
    h.columns = ['count']
    h = h.sort_values(by='count', ascending=False).head(head)
    
    return(h)

Co-Drivers Count per Gene

In [134]:
get_major_codrivers(master=master_hotspot_cnloh,
                    maf=maf_cohort_nowgd,
                    head=10)
Out[134]:
count
Hugo_Symbol
IDH1 77
ATRX 65
TERT 32
PTEN 17
RB1 13
PIK3CA 12
PIK3R1 9
NF1 8
ARID2 6
H3F3A 6
In [106]:
get_major_codrivers(master=master_hotspot,
                    maf=maf_cohort_nowgd,
                    head=10)
Out[106]:
count
Hugo_Symbol
TERT 137
IDH1 97
ATRX 76
PTEN 67
RB1 43
NF1 28
PIK3CA 25
PIK3R1 23
EGFR 20
ARID2 10

In comparison with entire Glioma Cohort:

  • Enrichment in IDH1 / ATRX
  • Depletion in TERT

Cut the cohort

In [113]:
symbol = 'IDH1'
master = master_hotspot_cnloh
maf=maf_cohort_nowgd

def get_master_codrivers(master: pd.DataFrame, maf: pd.DataFrame, symbol: str):
    samples = master.Tumor_Id.tolist()
    samples_final = maf[maf.Tumor_Sample_Barcode.isin(samples)][maf['Hugo_Symbol'] == symbol].Tumor_Sample_Barcode.tolist()

    master_filtered = master[master.Tumor_Id.isin(samples_final)]
    
    return master_filtered
In [127]:
master_IDH1 = get_master_codrivers(master=master_hotspot_cnloh,
                                   maf=maf_cohort_nowgd,
                                   symbol='IDH1')

master_ATRX = get_master_codrivers(master=master_hotspot_cnloh,
                                   maf=maf_cohort_nowgd,
                                   symbol='ATRX')

master_TERT = get_master_codrivers(master=master_hotspot_cnloh,
                                   maf=maf_cohort_nowgd,
                                   symbol='TERT')
In [128]:
master_hotspot_cnloh.frac_genome_altered.median()
Out[128]:
0.186
In [129]:
master_IDH1.frac_genome_altered.median()
Out[129]:
0.138
In [130]:
master_ATRX.frac_genome_altered.median()
Out[130]:
0.154
In [131]:
master_TERT.frac_genome_altered.median()
Out[131]:
0.277
In [135]:
master_hotspot_cnloh['data'] = '>=1_cnLOH'
master_IDH1['data'] = 'IDH1'
master_ATRX['data'] = 'ATRX'
master_TERT['data'] = 'TERT'

masters = [master_hotspot_cnloh, master_IDH1, master_ATRX, master_TERT]
allMasters = pd.concat(masters)
In [139]:
plt.figure()
allMasters[['frac_genome_altered', 'data']].boxplot(by="data")
Out[139]:
<matplotlib.axes._subplots.AxesSubplot at 0x14d059650>
<Figure size 600x600 with 0 Axes>

Clinical Correlates

Age

In [49]:
#### TP53 Residual Groups#fig=plt.figure(figsize=(10,3))
ax = plt.subplot2grid(shape=(4,1), loc=(0,0), colspan=1)

sns.boxplot(x='Patient_Current_Age',data=master_no_wgd_cancer, ax=ax).set_title('Patient Age - {}'.format(cancer), weight='bold', fontsize=14)


style(ax)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

TP53 Residual Groups

In [50]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_res_group',
                  palette=palette_res,
                  order=res_group_list,
                  metrics='Patient_Current_Age',
                  figsize=(3,10),
                  title='Patient Current Age - {}'.format(cancer),
                  xlim=[20,100])
plt.show()

get_statistics(df=master_no_wgd_cancer,
               group='tp53_res_group',
               metrics='Patient_Current_Age',
               group_list=['tp53_res', 'no_tp53_res'])#### TP53 Subgroups
Out[50]:
0 1 2
0 size Patient_Current_Age
1 tp53_res 120 61
2 no_tp53_res 277 48
3 Statistics p-value
4 6.01106 1.84313e-09

TP53 Subgroups

In [51]:
fig, ax = boxplot_sampletype(df=master_no_wgd_cancer,
                  group='tp53_group',
                  palette=palette,
                  order=group_list,
                  metrics='Patient_Current_Age',
                  figsize=(7,10),
                  title='Patient Current Age - {}'.format(cancer),
                  xlim=[20,100])
plt.show()

get_statistics(df=master_no_wgd_cancer,
               group='tp53_group',
               metrics='Patient_Current_Age',
               group_list=['1_WILD_TYPE', '>=1_cnLOH'])
Out[51]:
0 1 2
0 size Patient_Current_Age
1 1_WILD_TYPE 63 61
2 >=1_cnLOH 128 44
3 Statistics p-value
4 5.62919 1.81058e-08

Sex

In [52]:
h = get_groupby(master_no_wgd_cancer,'Sex', 'count').sort_values(by='count', ascending=False)
display(h)

h = h.T
h = h[['Male', 'Female']]
fig = plt.figure(figsize=(6,1))
ax = plt.subplot()

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

h_plot = h.plot(kind = 'barh', stacked=True, yticks=[], ax=ax)
ax.legend(['Male', 'Female'],loc='center left', bbox_to_anchor=(1.1, 0.5), fontsize=11)
ax.set_title('Sex Distribution - {} - No WGD'.format(cancer), weight='bold', fontsize=18)

plt.show()
count
Sex
Male 704
Female 484

Survival Analysis

In [389]:
from lifelines import KaplanMeierFitter
from lifelines.statistics import logrank_test
In [390]:
data = master_no_wgd_cancer.dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)

data = data[['tp53_group', 'tp53_res_group', 'Overall Survival Status 0/1', 'Overall_Survival_Months']]

ix1 = data['tp53_res_group'] == 'tp53_res'
ix2 = data['tp53_res_group'] == 'no_tp53_res'

T_exp, E_exp = data.loc[ix1, 'Overall_Survival_Months'], data.loc[ix1, 'Overall Survival Status 0/1']
T_con, E_con = data.loc[ix2, 'Overall_Survival_Months'], data.loc[ix2, 'Overall Survival Status 0/1']

results = logrank_test(T_exp, T_con, event_observed_A=E_exp, event_observed_B=E_con)
results.print_summary()
t_0 -1
null_distribution chi squared
degrees_of_freedom 1
test_name logrank_test
test_statistic p
0 4.54 0.03
In [54]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {}'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(res_group_list, range(len(res_group_list))):
    data = master_no_wgd_cancer[master_no_wgd_cancer['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
    kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
    kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
plt.show()
In [55]:
fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {}'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(group_list, range(len(group_list))):
    data = master_no_wgd_cancer[master_no_wgd_cancer['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
    kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
    kmf.plot_survival_function(color = palette_list[i], ax=ax)
plt.show()

Co-Drivers

IDH1
In [391]:
master_IDH1 = get_master_codrivers(master=master_no_wgd_cancer,
                                   maf=maf_cohort_nowgd,
                                   symbol='IDH1')

fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - IDH1'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(res_group_list, range(len(res_group_list))):
    data = master_IDH1[master_IDH1['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    try:
        data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
        kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
        kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
    except: pass
plt.show()




fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - IDH1'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(group_list, range(len(group_list))):
    data = master_IDH1[master_IDH1['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    try:
        data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
        kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
        kmf.plot_survival_function(color = palette_list[i], ax=ax)
    except: pass
plt.show()
TERT
In [392]:
master_TERT = get_master_codrivers(master=master_no_wgd_cancer,
                                   maf=maf_cohort_nowgd,
                                   symbol='TERT')

fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - TERT'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(res_group_list, range(len(res_group_list))):
    data = master_TERT[master_TERT['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    try:
        data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
        kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
        kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
    except: pass
plt.show()




fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - TERT'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(group_list, range(len(group_list))):
    data = master_TERT[master_TERT['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    try:
        data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
        kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
        kmf.plot_survival_function(color = palette_list[i], ax=ax)
    except: pass
plt.show()
ATRX
In [393]:
master_ATRX = get_master_codrivers(master=master_no_wgd_cancer,
                                   maf=maf_cohort_nowgd,
                                   symbol='ATRX')

fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - ATRX'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(res_group_list, range(len(res_group_list))):
    data = master_ATRX[master_ATRX['tp53_res_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    try:
        data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
        kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
        kmf.plot_survival_function(color = res_palette_list[i], ax=ax)
    except: pass
plt.show()




fig = plt.figure(figsize=(10,7))
ax = fig.add_subplot(111)
fig.suptitle('Survival Analysis - {} - ATRX'.format(cancer), fontsize=16, weight='bold')
kmf = KaplanMeierFitter()

for group,i in zip(group_list, range(len(group_list))):
    data = master_ATRX[master_ATRX['tp53_group'] == group].dropna(subset=['Overall_Survival_Months', 'Overall_Survival_Status'])
    try:
        data['Overall Survival Status 0/1'] = data.apply(lambda x: 1 if x['Overall_Survival_Status'] == 'DECEASED' else 0, axis=1)
        kmf.fit(np.array(data['Overall_Survival_Months']), event_observed=np.array(data['Overall Survival Status 0/1']),  label= group)
        kmf.plot_survival_function(color = palette_list[i], ax=ax)
    except: pass
plt.show()
In [ ]: